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Multiple Mobile Robots System (MMRS) has shown many attractive features in lots of real-world applications that motivate their rapid and wide diffusion. In MMRS, the Cooperative Localization (CL) is the basis and premise of its high-performance task. However, the statistical characteristics of the system noise should be already known in traditional CL algorithms, which is difficult to satisfy in actual MMRS because of the numerous of disturbances form the complex external environment. So the CL accuracy will be reduced. To solve this problem, an improved Adaptive Active Cooperative Localization (A2CL) algorithm based on information optimization strategy for MMRS is proposed in this manuscript. In this manuscript, an adaptive information fusion algorithm based on the variance component estimation under Extended Kalman filter (VCEKF) method for MMRS is introduced firstly to enhance the robustness and accuracy of information fusion by estimating the covariance matrix of the system noise or observation noise in real time. Besides, to decrease the effect of observation uncertainty on CL accuracy further, an observation optimization strategy based on information theory, the Model Predictive Control (MPC) strategy, is used here to maximize the information amount from observations. And semi-physical simulation experiments were carried out to verity the A2CL algorithm's performance finally. Results proved that the presented A2CL algorithm based on information optimization strategy for MMRS cannot only enhance the CL accuracy effectively but also have good robustness. [ABSTRACT FROM AUTHOR]

Abstract Incorporating Battery Battery Energy Storage System (BESS) with wind farm to build up Wind-Storage Combined Generation System is a promising solution to improve the dependability of wind power. BESS sizing and generation scheduling optimization are the key points of this process to improve the economy and efficiency of the integrated combined system. In this paper, based on Quantization Index (QI) clustering, the fluctuation feature of real-time wind power output is studied to obtain the most economic capacity as well as maximum charging/discharging power of BESS in every scheduling circle (15min). Thus, this size of BESS is taken as parameters and constraints for MPC simulation process to minimize the deviation between generation scheduling plan and actual integrated power of Combined Generation System. The optimization results of case study shows that both economic size and ordinary size performs well for generation scheduling optimization, while the proposed BESS sizing method can reduce invest cost to a large extent. [ABSTRACT FROM AUTHOR]

Energy management strategy is vital for a plug-in hybrid electric vehicle and in this paper, a strategy based on stochastic model predictive control is proposed. Firstly, Markov Chain Monte Carlo Simulation is used to predict velocity sequences in the 10-second horizon followed by post-processing like average filtering, quadratic fitting, etc. which is meant to moderate fluctuations of the results. The RMSE is controlled around 2.4357 Km/h. Moreover, dynamic programming is adopted to construct a benchmark strategy and also to act as the rolling optimization part of SMPC-based strategy. The results show that the fuel economy of the strategy based on SMPC is around 13 percent worse than that on DP. However, with 14.7 L/100 km as fuel consumption, it is still within reasonable ranges. [ABSTRACT FROM AUTHOR]

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